自我中心分层视觉语义
Egocentric Hierarchical Visual Semantics
AI总结：
本文提出一种遵循人类词汇层级语义结构的物体识别算法，通过递归识别视觉属与种差，实现机器感知与用户术语对齐，并给出初步评估。
摘要：
我们关注于对齐人们对物体的思考方式与机器所感知的内容，这意味着机器执行的物体识别应遵循一个类似于人类在思考与某一概念相关联的物体时所遵循的过程。最终目标是构建能够与用户进行有意义交互的系统，用用户自己的术语描述其所感知的内容。根据词汇语义学领域，人类将词语的意义组织在层级结构中，例如，一个名词的意义是通过一个更一般的名词（其属）以及一个或多个区分属性（其种差）来定义的。本文的主要宗旨是，物体识别应实现一个遵循用于定义词语意义的层级语义结构的分层过程。我们通过实现一种算法来实现这一目标，该算法对于任何物体，递归地识别其视觉属和视觉种差。换句话说，物体的识别被分解为一系列步骤，在这些步骤中识别局部相关的视觉特征。本文介绍了该算法及初步评估。
英文摘要：
We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an object associated with a certain concept. The ultimate goal is to build systems which can meaningfully interact with their users, describing what they perceive in the users' own terms. As from the field of Lexical Semantics, humans organize the meaning of words in hierarchies where the meaning of, e.g., a noun, is defined in terms of the meaning of a more general noun, its genus, and of one or more differentiating properties, its differentia. The main tenet of this paper is that object recognition should implement a hierarchical process which follows the hierarchical semantic structure used to define the meaning of words. We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia. In other words, the recognition of an object is decomposed in a sequence of steps where the locally relevant visual features are recognized. This paper presents the algorithm and a first evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
